3D city models

3D city models are digital models of urban areas Represent That land surfaces, sites, buildings, vegetation, infrastructure and landscape Elements as well as related objects (eg, city furniture) Belonging to urban areas. Their components are represented by two-dimensional and three-dimensional spatial data and geo-referenced data. 3D city models support, presentation, exploration, analysis, and management. In particular, 3D city models allow “for visually integrating heterogeneous geoinformation within a single framework and, therefore, create and manage complex urban information spaces.” [1] [2]

Storage of 3D City Models

To store 3D city models, both file-based and database approaches are used. There is no single, unique representation schema due to the heterogeneity and diversity of 3d city model contents.

Encoding of Components

Components of 3D city models are encoded by common file formats and Exchange for 2D raster-based GIS data (eg, GeoTIFF ), 2D vector-based GIS data (eg, AutoCAD DXF ), 3D models (eg, .3DS , OBJ ) , And 3D scenes (eg, Collada , Keyhole Markup Language ), such as CAD , GIS , and computer graphics tools and systems. All components of a 3D city model have to be transformed into a common geographic coordinate system .

Databases

GIS modeling and analysis tasks. A database for 3D city models. For example, the 3D City Database is a free 3D geo database to store, represent, and manage virtual 3D city models on a standard spatial relational database. [3]A database is required if 3D city models have to be continuously managed. 3D modeling and modeling of a 3D model of a 3D model. [4] Their implementation requires a multitude of formats (eg, based on FME multi-formats). As common application, Geodata download portals can be set up for 3D city model contents (eg, virtualcityWarehouse). [5]

CityGML

The Open Geospatial Consortium (OGC) defines an explicit XML -based exchange format for 3D city models, CityGML , which supports not only geometric descriptions of 3D city model components but also the specification of semantics and topology information. [6]

Construction of 3D City Models

Level of Detail

3D models are typically constructed at various levels of detail (LOD) to provide notions of multiple resolutions and at different levels of abstraction. Other metrics such as the level of spatio-semantic coherence and resolution of the texture can be considered a part of the LOD. [7] For example, CityGML defines five LODs for building models:

  • LOD 0: 2.5D footprints
  • LOD 1: Buildings represented by block models (usually extruded footprints)
  • LOD 2: Building models with standard roof structures
  • LOD 3: Detailed (architectural) building models
  • LOD 4: LOD 3 building models supplemented with interior features.

There exist also approaches to generalize a given detailed 3D city model by means of automated generalization. [8] For example, a hierarchical road network (eg, OpenStreetMap ) can be used to group Each cell is abstracted by aggregating and merging contained components.

GIS Data

GIS data modeling and modeling for a geo-referenced data model. GIS data also includes cadastral data that can be converted into simple 3D models as, for example, in the case of extruded building footprints. Core Components of 3D Modeling Models (DTM) represented, for example, by TINs or grids.

CAD Data

Typical sources of data for 3D city models also include CAD models of buildings, sites, and infrastructure elements. They provide a high level of detail, possible not by 3D city model applications, but can be incorporated either by exporting their geometry or as encapsulated objects.

BIM Data

Building information about a building, a building, a building, a building, a building, a building, a building, a building or a building.

Integration at Visualization Level

Complex 3D city models are typically based on GIS, building and site models from CAD and BIM. It is one of the most commonly used models of geo-spatial and geo-referenced data. The integration is possible by sharing a common geo-coordinate system at the visualization level. [9]

Building Reconstruction

Polygons of buildings, eg, taken from the cadaster, by pre-compute average heights. 3D point clouds (eg, sampled by terrestrial or aerial laser scanning) or by photogrammetric approaches. To achieve a high percentage of geometrically and topologically correct 3D building models, digital terrain surfaces and 2D footprint polygons are required by automated building reconstruction tools such as BREC. [10] One key challenge is to find building parts with their corresponding roof geometry. “Since fully automatic image understanding is very hard to solve,

Fully automated processes exist to generate LOD1 and LOD2 building models for large regions. For example, the Bavarian Office for Surveying and Spatial Information is responsible for about 8 million building models at LOD1 and LOD2. [14]

Visualization of 3D City Models

The visualization of 3D city models represents a core functionality required for interactive applications and systems based on 3D city models.

Real-Time Rendering of 3D City Models

Providing high quality visualization of massive 3D models in a scalable, fast, and costly manner is still a challenging task due to the complexity in terms of 3D geometry and textures of 3D city models. Real-time rendering provides a large number of specialized 3D rendering techniques for 3D city models. Examples of specialized real-time 3D rendering include:

  • Real-time 3D rendering of road networks on high resolution terrain models. [15]
  • Real-time 3D rendering of water surfaces with cartography-oriented design. [16]
  • Real-time 3D rendering of day and night sky phenomena. [17]
  • Real-time 3D rendering of grid-based terrain models. [18]
  • Real-time 3D rendering using different levels of abstraction, ranging between 2D and 3D views. [19]
  • Real-time 3D rendering of multiple views on 3D city models. [20] [21]

Real-time rendering algorithms and data structures are listed by the virtual field project. [22]

Service-Based Rendering of 3D City Models

Service-oriented architectures (SOA) for visualizing 3D city models. For SOA-based approaches, 3D portrayal services [23] are required, whose main functionality represents the portrayal in the sense of 3D rendering and visualization. [24] SOA-based approaches can be distinguished in two main categories, currently discussed in the Open Geospatial Consortium :

  • Web 3D Service (W3DS): This type of service handles geodata applications and mapping to computer graphics primitives such as scenes with textured 3D geometry models. The client applications are responsible for the 3D rendering of delivered graphs, ie, they are responsible for the interactive display using their own 3D graphics hardware.
  • Web View Service (WVS): This type of service encapsulates the 3D rendering process for 3D city models at the server side. (Eg, virtual panoramas or G-buffer cube maps [25] ), which are streamed and uploaded to requesting client applications. The client applications are responsible for re-building the 3D scene based on the intermediate representations. 3D applications for a 3D model of a 3D model. A 3D model of a 3D model.

Map-Based Visualization

A map-based technique, the “smart map” approach, targets at providing “massive, virtual 3D model websites, smartphones or tablets, by means of an interactive map assembled from arti fi cial oblique image tiles.” [26] The 3D map of the 3D city model; The map tiles, generated for different levels-of-detail, are stored on the server. This way, the 3D rendering is completed on the server’s side, simplifying access and use of 3D city models. The 3D rendering process can apply advanced rendering techniques (eg, global illumination and shadow calculation, illustrative rendering). Most importantly, The map-based approach allows for distributing and using complex maps. This way, “(a) The complexity of the 3D city model is decoupled from data transfer complexity (b) the implementation of client applications is simpli fi ed. For and by a large number of competitors, leading to a high degree of scalability of the overall approach. ” [27]

Applications

3D city models can be used for a multitude of applications in a growing number of different applications domains. [28] Examples:

  • Navigation systems : 3D navigation maps, which include 3D models, in particular, terrain models and 3D building models, to enhance the visual depiction and to simplify the recognition of locations. [29]
  • Urban Planning and Architecture : To set up, analyze, and disseminate urban planning concepts and projects. [30] 3D city models provide means for project communication, better acceptance of development projects through visualization, and therefore avoid monetary loss through project delays; They also help to prevent planning errors. [31]
  • Spatial Data Infrastructures (SDIs): 3D models and spatial models; They require not only tools and processes for the initial construction and storage of 3D models but also to provide workflows and applications. [32]
  • GIS : GIS 3D support geodata and computational algorithms to construct, transform, validate, and analyze
  • Emergency Management : For computational frameworks. In particular, they serve to simulate fire, floodings, and explosions. For example, the DETORBA project aims at simulating and analyzing effects of explosion in urban areas at high precision to support predictions of the structural integrity and soundness of the urban infrastructure and safety Preparations of rescue forces. [33]
  • Spatial Analysis : 3D city models provide the computational framework for 3D spatial analysis and simulation. For example, They Can be used to compute solar potential for 3D roof surfaces of cities, [34] visibility analysis dans le urban space, [35] noise simulation [36] thermographic inspections of buildings [37] [38]
  • Geodesign : In geodesign, virtual 3D models of the environment.
  • Gaming : 3D city models can be used to get basic data for virtual 3D scenes used in online and video games.
  • Cultural heritage : The role of cultural heritage in the design and development of cultural heritage. For example, archeological data can be embedded in 3D city models. [39]
  • City Information Systems: 3D city models represent the framework for interactive 3D city information systems and 3D city maps. For example, municipalities apply. [40]
  • Property management : real estate and real estate management.
  • Intelligent Transportation Systems : 3D city models can be applied to intelligent transportation systems. [41]
  • Augmented Reality : 3D city models can be used as a frame for augmented reality applications. [42]

References

  1. Jump up^ J. Döllner, K. Baumann and H. Buchholz: Virtual 3D City Models as a Foundation for Complex Urban Information Spaces (REAL CORP), Manfred Schrenk, Ed.), CORP – Competence Center of Urban and Regional Planning, pp. 107-112, 2006. ” (PDF) .
  2. Jump up^ “Example video for 3D city models as complex information spaces” .
  3. Jump up^ “3D City DB Web Site www.3dcitydb.org” .
  4. Jump up^ “virtual city database for 3D city and landscape models” .
  5. Jump up^ “virtualcityWarehouse” .
  6. Jump up^ TH Kolbe:Representing and Exchanging 3D City Models with CityGML. 3D Geo-Information Sciences, J. Lee, S. Zlatanova, W. Cartwright, G. Gartner, L. Meng, and MP Peterson, Eds. Springer Berlin Heidelberg, 2009, pp. 15-31
  7. Jump up^ Biljecki, F .; Ledoux, H .; Stoter, J .; Zhao, J. (2014). “Formalization of the level of detail in 3D city modeling”. Computers, Environment and Urban Systems . 48 (1): 1-15. Doi : 10.1016 / j.compenvurbsys.2014.05.004 .
  8. Jump up^ T. Glander, J. Döllner:Techniques for Generalizing Building Geometry of Complex Virtual 3D City Models. Advances in 3D Geoinformation Systems, (Peter van Oosterom and Sisi Zlatanova and Friso Penninga and Elfriede M. Fendel, ed.), Lecture Notes in Geoinformation and Cartography, Springer, pp. 381-400, 2008.
  9. Jump up^ J. Döllner, B. Hagedorn:Integrating Urban GIS, CAD, and BIM Data By Service-Based Virtual 3D City-Models. Urban and Regional Data Management: UDMS 2007 Annual, (Taylor & Francis Ltd, Stuttgart, Germany, pp. 157-170, 2007.
  10. Jump up^ http://www.virtualcitysystems.de/en/products/buildingreconstruction
  11. Jump up^ N. Haala, M. Kada: An update on automatic 3D reconstruction building. ISPRS Journal of Photogrammetry and Remote Sensing 65 (2010), 570-580.
  12. Jump up^ H. Huang, C. Brenner, M. Sester:3D building roof reconstruction from clouds via generative models. GIS 2011: 16-24.
  13. Jump up^ “K. Hammoudi: Contributions to the 3D city modeling: 3D polyhedral building model reconstruction from aerial images and 3D facade modeling from terrestrial 3D point cloud and images . , 234p., 2011. ” .
  14. Jump up^ “Bavarian LOD2 Building Model Project” .
  15. Jump up^ M. Vaaraniemi, M. Treib, R. Westermann:High-Quality Cartographic Roads on High-Resolution DEMs. Journal of WSCG 19 (2): 41-48, 2011.
  16. Jump up^ A. Semmo et al .:Real-Time Rendering of Water Surfaces with Cartography-Oriented Design. Proceedings International Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging (CAe), pp. 5-14, 2013.
  17. Jump up^ D. Limberger et al .:Single-Pass Rendering of Day and Night Sky Phenomena. Proceedings of the Vision, Modeling, and Visualization Workshop 2012, Eurographics Association, pp. 55-62, 2012.
  18. Jump up^ F. Losasso, H. Hoppe:Geometry clipmaps: Terrain rendering using nested regular grids. ACM Trans. Graphics (SIGGRAPH), 23 (3), 2004.
  19. Jump up^ https://www.youtube.com/watch?v=tU5d6WuSglk
  20. Jump up^ https://www.youtube.com/watch?v=bT01QsZMYDE
  21. Jump up^ http://www.hpi.uni-potsdam.de/doellner/publications/year/2014/2390/PSTD2014.htmlMultiperspective Views for 3D City Models
  22. Jump up^ http://vterrain.org/LOD/Papers/
  23. Jump up^ http://www.opengeospatial.org/projects/initiatives/3dpie
  24. Jump up^ J. Klimke, J. Döllner:Service-oriented Visualization of Virtual 3D City Models. Directions Magazine, 2012.http://www.directionsmag.com/articles/service-oriented-visualization-of-virtual-3d-city-models/226560
  25. Jump up^ J. Döllner, B. Hagedorn:Server-Based Rendering of Large 3D Scenes for Mobile Devices Using G-Buffer Cube Maps. Web3D ’12 Proceedings of the 17th International Conference on 3D Web Technology, pp. 97-100, 2012.
  26. Jump up^ J. Klimke et al .: “Scalable Multi-Platform Distribution of Spatial 3D Contents”. ISPRS 8th 3D GeoInfo Conference & WG II / 2 Workshop 27-29 November 2013, Istanbul, Turkey, (U. Isikdag, ed.), Vol. II-2 / W1, ISPRS Annals, ISPRS, pp. 193-200, 2013.
  27. Jump up^ J. Klimke et al .: “Scalable Multi-Platform Distribution of Spatial 3D Contents”. ISPRS 8th 3D GeoInfo Conference & WG II / 2 Workshop 27-29 November 2013, Istanbul, Turkey, (U. Isikdag, ed.), Vol. II-2 / W1, ISPRS Annals, ISPRS, pp. 193-200, 2013.
  28. Jump up^ Biljecki, F .; Stoter, J .; Ledoux, H .; Zlatanova, S .; Çöltekin, A. (2015). “Applications of 3D City Models: State of the Art Review”. ISPRS International Journal of Geo-Information . 4 (4): 2842-2889. Doi : 10.3390 / ijgi4042842 .
  29. Jump up^ M. Vaaraniemi et al .:Intelligent Prioritization and Filtering of Labels in Navigation Maps. Journal of WSCG, 2014.
  30. Jump up^ “E. Ben-Joseph et al .: Urban simulation and the luminous planning table: Bridging the gap between the digital and the tangible, Journal of Planning Education and Research 21 (2), 196-203, (PDF) .
  31. Jump up^ http://www.gsdi.org/gsdiconf/gsdi12/slides/2.4a.pdf
  32. Jump up^ http://virtualcitysystems.de/images/pdf/3d-gdi/EN_3D_SDI_2013_Flyer.pdf
  33. Jump up^ http://virtualcitysystems.de/en/references.html#researchDETORBA
  34. Jump up^ C. Carneiro et al .:Solar radiation over the urban texture: LiDAR data and image processing techniques for environmental analysis at city scale. 3D Geo-Information Sciences, 319-340, 2008.
  35. Jump up^ J. Engel, J. Döllner:Approaches Towards Visual 3D Analysis for Digital Landscapes and Its Applications. Digital Landscape Architecture Proceedings 2009, pp. 33-41, 2009.
  36. Jump up^ “M. Lancelle and D. Fellner,” Current Issues on 3D City Models, “Proc. Image and Vision Computing, 2004, pp. 363-369” (PDF) .
  37. Jump up^ “D. Iwaszczuk et al .: Matching of 3D building models with IR images for texture extraction JURSE 2011 – Joint Urban Remote Sensing Event, 25-28” .
  38. Jump up^ L. Hoegner et al .:Automatic extraction of textures from infrared image sequences and database integration for 3D building models. PFG Photogrammetry Fernerkundung Geoinformation, 2007 (6): 459-468, 2007.
  39. Jump up^ Trapp et al .:Colonia 3D – Communication of Virtual 3D Reconstructions in Public Spaces. International Journal of Heritage in the Digital Era (IJHDE), Vol. 1, no. 1, p. 45-74, 2012.
  40. Jump up^ http://www.businesslocationcenter.de/en/berlin-economic-atlas/the-project
  41. Jump up^ “IEEE Intelligent Transportation Systems Society” .
  42. Jump up^ C. Portalés et al .: Augmented reality and photogrammetry: A synergy to visualize physical and virtual city environments ISPRS J. Photogramm Remote Sensing, 65, 134-142, 2010. ” (PDF) .

Leave a Comment

Your email address will not be published. Required fields are marked *